Revenue cycle management best practices: a guide for success

by Experian Health 5 min read August 13, 2024

Maintaining a healthy cash flow is the only way to deliver quality patient care, invest in state-of-the-art technologies and keep daily operations running smoothly. But that’s easier said than done: data errors, delayed payments, denials and staffing disruptions leave providers vulnerable to escalating admin costs and revenue leakage, with little left over to reinvest. By adopting a few key revenue cycle management (RCM) strategies, providers can sidestep these challenges and bring in more dollars. This guide summarizes five revenue cycle management best practices healthcare leaders should follow to optimize RCM workflows and promote financial stability.

Key challenges in revenue cycle management

Common issues that can get in the way of a healthy revenue flow include:

  • Inaccurate patient data leading to coding errors, claim denials and billing delays
  • Increasing numbers of denied claims generate costly rework and wasted time
  • Payer compliance issues that are constantly changing and time-consuming to monitor
  • Growing numbers of self-pay patients struggling to pay their bills
  • Labor shortages increase pressure on staff and leave the door open to sub-par performance
  • Inadequate data insights hindering management’s ability to spot opportunities for improvement
  • Rapid technological changes leave providers on the back foot if they fail to keep pace with new developments.

The dream scenario would be to avoid all these potential obstacles before they do too much damage. In reality, providers will need to choose a few priority areas to troubleshoot.

Check out this guide to choosing the right key performance indicators for your revenue cycle dashboard to ensure the effective implementation of RCM strategies.

Revenue cycle management best practices

What does a successful revenue cycle look like? For busy RCM leaders, deciding what to tackle first can be overwhelming. While there’s no one-size-fits-all RCM strategy, there are a few key issues that all organizations must pay attention to. Here are five areas of best practice to factor in:

1. Streamline patient registration and insurance verification

Accurate patient data is the number one factor in building a robust revenue cycle. It doesn’t matter how efficient claims management and collections processes are if the data they use is flawed. Automated registration and verification tools reduce the chances of manual errors entering the system to ensure correct billing, reduce denials, and speed up reimbursement.

One pitfall to watch out for is the fact that some digital tools still require staff to check multiple payer websites and data repositories to verify insurance eligibility. Experian Health’s latest patient access solution, Patient Access Curator, avoids this by using AI-driven technology to collect and verify patient information with a single click.

2. Automate claims submission and management

According to Experian Health’s State of Claims 2022 report, 62% of providers feel they lack the necessary data and analytics to identify issues in claims submission processes. A similar number believe the absence of automation prevents improvement. The CAQH Index backs this up, with the latest estimates suggesting the healthcare industry could save $18.3 billion by switching to electronic transactions.

As with patient intake, there’s an opportunity to leverage automation in claims management to prevent errors and delays so the organization gets paid faster. Experian Health’s claims management products—ranked #1 in 2024 surveys by both KLAS and Black Book—automate each step of the claims workflow so providers can submit clean claims quickly and cut the need for time-consuming manual work.

3. Optimize denials management and appeals with AI

Despite best efforts, claims denials remain a burden for many RCM teams. However, proactively understanding and addressing the root causes can help keep denials under control. There’s an opportunity to go a step beyond automation and see how artificial intelligence and machine learning can help combat the denials challenge.

AI AdvantageTM evaluates individual claims in real time to flag those with a high likelihood of denials based on historical payment data, so staff can intervene quickly before submission. Denials are then triaged using advanced algorithms so staff can focus on reworking denials with the greatest chance of payment, rather than wasting time on those that are never going to be approved.

Eric Eckhart, Director of Patient Financial Services at Community Medical Centers in California, says that since implementing AI Advantage, “Now I have almost a whole week a month of staff time back, and I can put that on other things. I can pull that back from outsourcing to other follow-up vendors and bring that in-house and save money. The savings have snowballed. That’s really been the biggest financial impact.”

Watch the webinar: Hear how Community Medical Centers and Schneck Medical Center are using AI AdvantageTM to prevent and triage denials.

4. Choose the right technology and tools for enhanced RCM

The three previous revenue cycle management best practices emphasize the importance of selecting the right tools for the task. Two things to look out for when adopting a new RCM product are how well it integrates with existing tools and systems, and whether it offers meaningful insights to drive ongoing improvements.

Experian Health’s integrated RCM solutions are designed to fit together seamlessly, often allowing staff to view information from multiple workflows within the same dashboard. By bringing together metrics such as financial performance, billing efficiency and collections rates into one place, these tools help staff make strategic decisions about resource allocation and operational improvements.

5. Keep up with regulatory compliance

Finally, ensuring compliance with regulatory requirements cannot be overlooked. The reputational and financial risks are too great. Regular training for staff on compliance issues and maintaining up-to-date knowledge of government and payer requirements will minimize the risk of penalties. Choosing RCM tools that automatically check for relevant updates can help providers stay current.

Price transparency is a topical example. While the Hospital Price Transparency Rule is designed to help healthcare consumers understand healthcare costs and make more informed decisions about their care, implementation has proven tricky for providers. With the right technology and third-party support, it’s much easier to stay compliant.

Watch the webinar: See how Experian Health and Cleverley & Associates have partnered to help healthcare organizations navigate price transparency in 2024.

Looking for more insights into revenue cycle management best practices? Contact Experian Health today to discover how our RCM solutions can transform your revenue cycle and increase cash flow year over year.

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Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

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